The present invention relates generally to estimating property values, and more particularly, to providing preprocessed property value estimates.
Financial institutions and businesses involved with sales of property have long tried to estimate values of property accurately. Accurate estimation serves many important purposes. For example, financial institutions use property value estimates as one of the key factors in approving mortgage applications for real estate sales. Relying on the soundness of the estimate, financial institutions accept the risk of lending large sums of money and typically attach the property as security for the transaction. Accordingly, the accuracy of estimated value of the real estate entity is critical.
In addition to the accuracy of the estimate, timeliness is a significant factor. For example, a closing on a real estate sales contract may depend on the buyer successfully obtaining a loan within a limited time period. Also, the ability to evaluate the value of a large number of properties in a short time frame is a business necessity. Bids on pools of seasoned loans, for example, are due in a very tight timeframe and require collateral evaluation. Hence, the ability to estimate the value of the real estate entity quickly is very important to lenders and prospective buyers.
According to current industry practice, an estimate for a particular property is produced by a system in real-time, but this poses many problems. For one, the process is slow, especially for processing bulk transmissions. The system also needs to validate the address of the property to be estimated to ensure that the input address is indeed a valid address. For example, the system may check against an existing database to determine whether the input address contains a valid zip code or whether a combination of city and state fields of the input address matches the zip code of the input address. Moreover, real-time processing is subject to unpredictable conditions such as availability of properly running equipment, appraiser availability, and scheduling conflicts, whenever estimates are necessary. Further, a system computes an estimate for a property each time a request is submitted, even for properties that have been previously computed. Such redundancy wastes processing time and resources, results in repeated data entry, and increases the cost of the property valuations.
Furthermore, in existing systems, the platform providing the valuation estimates also needs access to “raw” bases. For example, the Hedonic model requires the capture of property level characteristic data and transaction value data to provide an estimate. This may involve either the use of large amounts of electronic data storage devices or connectivity with other systems, such as a data provider's system, which results in either additional cost and/or a reduction of reliability. In the case of the repeat sales model, after the growth rate table is created it is applied to the “seed value data set” to create, the forecasts.
Therefore, it is desirable to increase efficiency of property value forecasts by streamlining the forecasting process.
It is also desirable to provide a timely and reliable estimate of value.